ArticleProceedings of the National Academy of Sciences of the United States of America2020
Predicting high-risk opioid prescriptions before they are given.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 52 citations in OpenAlex.
- Developing and Validating a Machine Learning Algorithm to Predict the Risk of Incident Opioid Use Disorder Among OneFlorida+ Patients: Prognostic Modeling Study.Journal of medical Internet research · 2026Article
- Development and evaluation of machine learning algorithms for the prediction of opioid-related deaths among UK patients with non-cancer pain.PLOS digital health · 2026Article
- Speak and You Shall Predict: Evidence That Speech at Initial Cocaine Abstinence Is a Biomarker of Long-Term Drug Use Behavior.Biological psychiatry · 2025Article
- A Review of Leveraging Artificial Intelligence to Predict Persistent Postoperative Opioid Use and Opioid Use Disorder and its Ethical Considerations.Current pain and headache reports · 2025Review
- Incarceration history and opioid use among adults living with HIV and chronic pain: a secondary analysis of a prospective cohort study.Health & justice · 2024Article
- Association of Average Daily Morphine Milligram Equivalents and Falls in Older Adult Chronic Opioid Users.Pharmacy (Basel, Switzerland) · 2024Article
- A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records.PloS one · 2024Article
- Complementarities between algorithmic and human decision-making: The case of antibiotic prescribing.Quantitative marketing and economics · 2024Article
- Individualized Prospective Prediction of Opioid Use Disorder.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2023Article
- Association of Postsurgical Opioid Refills for Patients With Risk of Opioid Misuse and Chronic Opioid Use Among Family Members.JAMA network open · 2022Article
- Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study.The Lancet. Digital health · 2022Article
- Evaluating algorithmic fairness in the presence of clinical guidelines: the case of atherosclerotic cardiovascular disease risk estimation.BMJ health & care informatics · 2022Article
- Using machine learning to study the effect of medication adherence in Opioid Use Disorder.PloS one · 2022Article
- Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Older, Heavier, Arthritic, Psychiatrically Disordered, and Opioid-Familiar Patients Are at Risk for Opioid Use After Medial Patellofemoral Ligament Reconstruction.Arthroscopy, sports medicine, and rehabilitation · 2021Article
- Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients.American journal of surgery · 2021Article
- Development of a machine learning algorithm for early detection of opioid use disorder.Pharmacology research & perspectives · 2020Article
- Predicting high-risk opioid prescriptions before they are given.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Misuse of prescription opioids is a leading cause of premature death in the United States. We use state government administrative data and machine learning methods to examine whether the risk of future opioid dependence, abuse, or poisoning can be predicted in advance of an initial opioid prescription. Our models accurately predict these outcomes and identify particular prior nonopioid prescriptions, medical history, incarceration, and demographics as strong predictors. Using our estimates, we simulate a hypothetical policy which restricts new opioid prescriptions to only those with low predicted risk. The policy's potential benefits likely outweigh costs across demographic subgroups, even for lenient definitions of "high risk." Our findings suggest new avenues for prevention using state administrative data, which could aid providers in making better, data-informed decisions when weighing the medical benefits of opioid therapy against the risks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.